System and method for detection of vehicle lane departure

ABSTRACT

A lane departure detection computing device for a roadside device is disclosed. The computing device is configured to execute instructions stored in a memory to: calculate a first path geometry relating to a first vehicle traveling in a first lane; calculate a second path geometry relating to a second vehicle traveling in a second lane different from the first lane; evaluate coextensive portions of the first path geometry and the second path geometry for parallelism; if the coextensive portions of the first path geometry and the second path geometry evaluated for parallelism are not parallel, determine that one of the first vehicle and the second vehicle is executing a lane departure; and operate a vehicle configured for autonomous operation responsive to the determination that one of the first vehicle and the second vehicle is executing a lane departure.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims the benefit of, U.S.application Ser. No. 14/997,550, filed on Jan. 17, 2016.

TECHNICAL FIELD

Aspects of the disclosure generally relate to a system including acomputing device and one or more sensors in communication with thecomputing device, the computing device being incorporated in a roadsidedevice, the system being configured to detect movement of one or morevehicles from one lane toward another lane.

BACKGROUND

Each vehicle driving on a roadway must interact with other nearbyvehicles. In high-speed, dynamic driving environments, a vehicle maysometimes change lanes without its driver being aware of the location ofall the vehicles driving in close proximity to the lane-changingvehicle. This can create potential hazards for vehicles driving close tothe lane-changing vehicle, and especially for vehicles drivingrelatively closely behind the lane-changing vehicle, in an adjacentlane. Thus, it would be beneficial to have a system that detects whenvehicles are changing lanes, and which can alert drivers of other,non-lane changing vehicles to the lane change.

Also, there are driving conditions where the actual lanes being used bytraffic are different from marked lanes. Such conditions may occur, forexample, due to road construction, temporary lane closure, or accidentinvestigation. These actual lanes being traveled by vehicles are stillconstrained by road geometry, but may differ from lanes marked byconventional lane markers. Furthermore, in some cases, conventional lanemarkers and road edges may be difficult to detect visually or usingcamera systems. Thus, it would be beneficial to have a system capable ofdetermining the road geometry and lane paths based on the actual travelpaths of vehicles and independent of normal lane boundary markers.

SUMMARY

In one aspect of the embodiments described herein, acomputer-implemented method is provided for operating a vehicleconfigured for autonomous operation. The method includes steps of (1) ina lane departure detection computing device of a roadside device: (a)calculating a first path geometry relating to a first vehicle travelingin a first lane; (b) calculating a second path geometry relating to asecond vehicle traveling in a second lane different from the first lane;(c) evaluating coextensive portions of the first path geometry and thesecond path geometry for parallelism; (d) if the coextensive portions ofthe first path geometry and the second path geometry evaluated forparallelism are parallel, repeating steps (a)-(d); (e) if thecoextensive portions of the first path geometry and the second pathgeometry evaluated for parallelism are not parallel, determining thatone of the first vehicle and the second vehicle is executing a lanedeparture; and (2) operating the vehicle configured for autonomousoperation responsive to the determination that one of the first vehicleand the second vehicle is executing a lane departure.

In another aspect of the embodiments of the described herein, anon-transitory computer readable medium is provided with computerexecutable instructions stored thereon and executed by a processor toperform a method of operating a vehicle configured for autonomousoperation. The method includes steps of (a) calculating a first pathgeometry relating to a first vehicle traveling in a first lane; (b)calculating a second path geometry relating to a second vehicletraveling in a second lane different from the first lane; (c) evaluatingcoextensive portions of the first path geometry and the second pathgeometry for parallelism; (d) if the coextensive portions of the firstpath geometry and the second path geometry evaluated for parallelism areparallel, repeating steps (a)-(d); (e) if the coextensive portions ofthe first path geometry and the second path geometry evaluated forparallelism are not parallel, determining that one of the first vehicleand the second vehicle is executing a lane departure; and (f) operatingthe vehicle configured for autonomous operation responsive to thedetermination that one of the first vehicle and the second vehicle isexecuting a lane departure.

In another aspect of the embodiments of the described herein, a lanedeparture detection computing device for a roadside device is provided.The computing device includes one or more processors for controllingoperation of the computing device, and a memory for storing data andprogram instructions usable by the one or more processors, wherein theone or more processors are configured to execute instructions stored inthe memory to: calculate a first path geometry relating to a firstvehicle traveling in a first lane; calculate a second path geometryrelating to a second vehicle traveling in a second lane different fromthe first lane; evaluate coextensive portions of the first path geometryand the second path geometry for parallelism; if the coextensiveportions of the first path geometry and the second path geometryevaluated for parallelism are not parallel, determine that one of thefirst vehicle and the second vehicle is executing a lane departure; andoperate a vehicle configured for autonomous operation responsive to thedetermination that one of the first vehicle and the second vehicle isexecuting a lane departure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments described herein andtogether with the description serve to explain principles of embodimentsdescribed herein.

FIG. 1 is a block diagram of one embodiment of a vehicle system fordetecting lane departures or lane changes of vehicles surrounding an egovehicle, without the use of lane marking information.

FIG. 2 is a block diagram of a computing device incorporated into thelane departure detection system of FIG. 1.

FIG. 3 is a block diagram showing of an ego-vehicle similar to thatshown in FIG. 1, in communication with one embodiment of a surroundingvehicle.

FIG. 4 is a schematic diagram illustrating a computer-implemented methodof generating a path geometry of an ego vehicle and a surroundingvehicle.

FIG. 5 is a schematic diagram showing additional aspects of the systemembodiments described herein.

FIG. 6 is a flow diagram illustrating a method for detecting a lanedeparture of a vehicle traveling in a lane adjacent an ego-vehicle, in ascenario such as shown in FIG. 4 and FIG. 5.

FIG. 7 is a schematic diagram illustrating a computer-implemented methodof generating path geometries of surrounding vehicles traveling in frontof an ego-vehicle.

FIG. 8 is a flow diagram illustrating a method for detecting a lanedeparture of one of two surrounding vehicles traveling ahead of theego-vehicle, in a scenario such as shown in FIG. 7.

FIG. 9 is a schematic diagram showing various possible alternativeimplementations of the lane departure detection system.

DETAILED DESCRIPTION

Embodiments of the lane departure detection system described hereinenable an ego-vehicle to detect lane changes of vehicles without the useof lane marking information on the road. The system calculates a pathgeometry of at least two vehicles traveling along a road in adjacentlanes. The system evaluates the most recent portions of the pathgeometries of the two vehicles for parallelism. If the evaluatedportions of the paths are deemed to be parallel, it is deemed unlikelythat both vehicles are changing lanes simultaneously. It is also deemedunlikely that any one of the two vehicles was changing lanes. Therefore,the system determines that no lane change is occurring. However, if theIf the evaluated portions of the paths are deemed to be non-parallel,the system determines that the paths followed by the associated vehiclesare no longer parallel. In this case, it the system assumes that one ofthe vehicles is changing lanes. In a particular application, the systemmay be used to detect a lane change of a vehicle driving ahead of theego vehicle in an adjacent lane. This enables a driver to takeappropriate responsive action and also enables an automated drivingsystem in the ego-vehicle to plan a route of the ego-vehicle. Sensors ofthe lane departure detection system and/or a computing systemincorporating the lane departure detection capabilities described hereinmay also be located in a roadside device configured for detecting lanechanges by vehicles driving along a section of road.

FIG. 1 is a block diagram of one embodiment of a vehicle system 200 fordetecting lane departures or lane changes of vehicles surrounding an egovehicle 12, without the use of lane marking information. System 200 isshown incorporated into an ego vehicle 12 and includes a known inertialnavigation system (INS) 16, vehicle sensors 14, and a computing device18 in operative communication with sensors 14 and INS 16.

INS 16 may also be configured for monitoring and storing in a memory apath of the vehicle 12 along a road. As is known in the pertinent art,the INS is configured for using vehicle sensor data to continuouslycalculate (by “dead reckoning”) the position, direction, velocity andacceleration of the vehicle 12 without the need of external references.Thus, the output of the INS 16 includes the coordinates (x, y, z, t) ofthe vehicle 11 in 3-D space and time t, as well as its instantaneousspeed (v) and acceleration (a). The INS may include (or may be inoperative communication with) any sensors 14 (such as motion sensors(for example, accelerometers), rotation sensors (for example,gyroscopes), etc.) suitable for the purposes described herein. Forexample, INS may include sensors configured for detecting the vehicle'sroll rate, yaw rate, pitch rate, longitudinal acceleration, lateralacceleration, vertical acceleration and other vehicle operationalparameters. The terms “continuous” or “continuously” and other similarterms as used herein with reference to the calculation, processing,receiving, evaluation, comparing and approximation of variousparameters, are understood to signify that these activities occur at themost rapid rate possible within the limitations of the system orcomponent capabilities.

Vehicle sensors 14 are configured to measure various vehicle parametersand to provide vehicle operational information to other vehiclecomponents, for example INS 16 and computing device 18. For the purposesdescribed herein, certain sensors are configured for detecting thepositions of any surrounding vehicles in relation to the position of theego vehicle (i.e., the vehicle in which the sensors are installed).Vehicle sensors 14 may include any sensors suitable for providing thedata of information usable for the purposes described herein. Examples(not shown) of sensors that may be incorporated into the vehicle 12include radar and lidar systems, laser scanners, vision/camera systems,GPS systems, various inertial sensors such as gyroscopes andaccelerometers, vehicle wheel speed sensors, road condition sensors,suspension height sensors, steering angle sensors, steering torquesensors, brake pressure sensors, accelerator or pedal position sensor,and tire pressure sensors.

As stated previously, certain vehicle sensors may be incorporated intoINS 16 or into other vehicle components or modules, and some sensors maybe mounted on the vehicle in a stand-alone configuration. The data orinformation provided by any of sensors 14 may be integrated or combinedwith other data or information in a sensor fusion step using, forexample, a suitable Kalman filter (not shown). Also, if required, dataor information transmitted within or to vehicle 12 may be processed inan A/D converter, D/A converter or other processing means (not shown)for example, prior to further processing or other operations performedon the information by other vehicle elements or systems.

FIG. 2 illustrates a block diagram of a computing device 18 in lanedeparture detection system 200 that may be used according to one or moreillustrative embodiments of the disclosure. The lane departure detectioncomputing device 18 may have one or more processors 103 for controllingoverall operation of the device 18 and its associated components,including RAM 105, ROM 107, input/output module or HMI (human machineinterface) 109, and memory 115. The computing device 18 may beconfigured for transmitting and receiving vehicle-to-vehicle (V2V)communications, and for collecting and/or receiving vehicle sensor dataand other information.

The computing device 18 is also configured for continuously receivingego vehicle position, direction, velocity and acceleration informationfrom the INS, for storing the ego-vehicle information in memory, and forcontinuously processing the ego-vehicle information to generate and/ormaintain a digital representation of the ego vehicle path geometry, asdescribed herein. The computing device 18 is also configured forcontinuously receiving and storing sensor information relating to thepositions of any surrounding vehicles (SV's) with respect to theego-vehicle over time, and for continuously calculating, using thesurrounding vehicle position information, the velocities andaccelerations of the surrounding vehicles with respect to the egovehicle over time. The computing device 18 is also configured forcontinuously processing the surrounding vehicle information as describedherein to generate digital representations of the path geometries of thesurrounding vehicles in relation to the path geometry of theego-vehicle. The computing device 18 is also configured for continuouslycomparing the path geometries of the surrounding vehicles to each otherand also to the path geometry of the ego vehicle as described herein,and for using the comparisons to detect lane departures or changes of asurrounding vehicle. The computing device 18 may also be configured forcommunication with various vehicle-based devices (e.g., on-board vehiclecomputers, short-range vehicle communication systems, telematicsdevices), mobile communication devices (e.g., mobile phones, portablecomputing devices, and the like), roadside stations or devices and/orother remote systems (such as GPS systems).

Software may be stored within memory 115 and/or storage to provideinstructions to processor 103 for enabling device 18 to perform variousfunctions. For example, memory 115 may store software used by the device18, such as an operating system 117, application programs 119, and anassociated internal database 121. Processor 103 and its associatedcomponents may allow the lane departure detection system 200 to executea series of computer-readable instructions to perform the functionsdescribed herein.

An Input/Output (I/O) or HMI (human-machine interface) 109 may include amicrophone, keypad, touch screen, and/or stylus through which a user ofthe computing device 18 may provide input, and may also include one ormore of a speaker for providing audio output and a video display devicefor providing textual, audiovisual and/or graphical output. HMI 109 mayalso be configured for providing output to mobile communication devices141 (e.g., mobile phones, portable computing devices, and the like).

FIG. 2 is a block diagram showing of an ego-vehicle 12 similar to thatshown in FIG. 1, in communication with one embodiment of a surroundingvehicle SV. Each component shown in FIG. 2 may be implemented inhardware, software, or a combination of the two. Vehicle SV may includea computing device 118 similar computing device 18 described previously.A Global Positioning System (GPS), locational sensors positioned insidethe vehicles 12 and SV, and/or locational sensors or devices external tothe vehicles 12 and SV may be used determine vehicle route, laneposition, vehicle movement and other vehicle position/location data.Additionally, the vehicle position and movement information and/or otherinformation may be transmitted via telematics devices 213 and 223 toother vehicles or to one or more remote systems or computing devices(not shown in FIG. 2).

Vehicle 12 may also incorporate a short-range communication system 212configured to transmit vehicle operational information and otherinformation to surrounding vehicles and/or to receive vehicleoperational information and/or other information from surroundingvehicles. The vehicle operational information may include informationrelating to vehicle position and movement necessary for performing thefunctions described herein. In some examples, communication system 212may use dedicated short-range communications (DSRC) protocols andstandards to perform wireless communications between vehicles. In theUnited States, 75 MHz in the 5.850-5.925 GHz band have been allocatedfor DSRC systems and applications, and various other DSRC allocationshave been defined in other countries and jurisdictions. However,short-range communication system 212 need not use DSRC, and may beimplemented using other short-range wireless protocols in otherexamples, such as WLAN communication protocols (e.g., IEEE 802.11),Bluetooth (e.g., IEEE 802.15.1), or one or more of the CommunicationAccess for Land Mobiles (CALM) wireless communication protocols and airinterfaces. The vehicle-to-vehicle (V2V) transmissions between theshort-range communication system 212 and other vehicles 222 may be sentvia DSRC, Bluetooth, satellite, GSM infrared, IEEE 802.11, WiMAX, RFID,and/or any suitable wireless communication media, standards, andprotocols.

In certain systems, short-range communication systems 212 and 222 mayinclude specialized hardware installed in vehicles 210 and 220 (e.g.,transceivers, antennas, etc.), while in other examples the communicationsystems 212 and 222 may be implemented using existing vehicle hardwarecomponents (e.g., radio and satellite equipment, navigation computers).The range of V2V communications between vehicle communication system 212and other vehicles may depend on the wireless communication standardsand protocols used, the transmission/reception hardware (e.g.,transceivers, power sources, antennas), and other factors. Short-rangeV2V communications may range from just a few feet to many miles. V2Vcommunications also may include vehicle-to-infrastructure (V2I)communications, such as transmissions from vehicles to non-vehiclereceiving devices, for example, toll booths, rail road crossings, androad-side devices. Certain V2V communication systems may continuouslybroadcast vehicle operational information from a surrounding vehicle orfrom any infrastructure device capable of transmitting the informationto an ego-vehicle.

A vehicle communication system 212 may first detect nearby vehiclesand/or infrastructure and receiving devices, and may initializecommunication with each by performing a handshaking transaction beforebeginning to receive vehicle operational information. New of additionalsurrounding vehicles may be detected in the ego-vehicle by any of avariety of methods, for example, by radar or by reception by theego-vehicle (from a surrounding vehicle configured for V2V communicationwith the ego-vehicle) of vehicle operational information from the newvehicle.

For purposes of the following discussion of FIG. 4, the ego-vehicle pathis considered to be path A, while path B represents the path of asurrounding vehicle traveling in proximity to the ego-vehicle.

FIG. 4 illustrates a computer-implemented method of generating arepresentation of a path geometry A of an ego vehicle 12. On acontinuous basis, during movement of the ego vehicle 12 on a road indirection G, the position, direction, velocity and acceleration of theego vehicle 12 are calculated by the INS 16 using sensor readings takenat successive points in time t₁, t₂, t₃, etc. For example, the positionof the ego-vehicle 12 at time t₁ is shown as P1, which represents the x,y, and z coordinates of the ego-vehicle at a time t₁. The values ofthese parameters are passed to computing device 18. During movement ofthe vehicle, the computing device 18 processes the positional data toconnect time-successive calculated vehicle positions P_(i) with linesegments l_(i) as shown in FIG. 4. The aggregate of the connected linesegments l_(i) are used, in one embodiment, to define the vehicle pathgeometry in terms of an up-to-date representation of a positional orpath history of the vehicle.

In embodiments described herein, for purposes of tracking the vehiclepaths as accurately as possible, it is desirable that the sensor datafor all vehicles be acquired at least a minimum predetermined samplingrate, to help minimize errors in the path geometry approximation caused,for example, by using one or more straight line or polynomial segmentsto approximate a curved portion of the path geometry. In one embodiment,the vehicle path is approximated by a string of linear segmentsconnecting the successive vehicle positions. For a path approximated bya connected string of linear segments, the minimum predeterminedsampling rate may be determined by specifying a maximum permitteddistance between sensor measurement locations (i.e., a maximum desirablelength of a line segment l_(i) connecting any two successive vehiclepositions P_(i) and P_(i+1)), according to the relationship:

Max |l _(i) |<L _(max) for all i in {1, 2, 3, 4, etc.}  (1)

where Max |l_(i)| is the maximum absolute value of the length of theline segment l_(i) and L_(max) is a parameter which may be determinedaccording to the requirements of a given application. Thus, the sensorsampling rate is determined such that the maximum length of a linesegment l_(i) connecting successive measured vehicle locations P_(i) andP_(i+1) is always below L_(max). In a particular embodiment, L_(max) isset to the greater of either 1.5 meters or 0.2 (v) meters, where v isthe speed of the vehicle in meters/second. In other embodiments, thepath geometry may be approximated in a known manner from the successivemeasured vehicle locations using spline interpolation or polynomialinterpolation.

In addition, for more accurate results, it is also desirable that thecoextensive path history portions being evaluated for parallelism bespaced apart at least a minimum distance determined according to thefollowing relationship:

D _(min)<max|d _(i) |<D _(max) for all i in {1, 2, 3, 4, etc.}  (2)

where D_(min)=a minimum spacing between the paths, D_(max)=a maximumspacing between the paths, and max |d _(i)| is an absolute measuredvalue between the paths. For the purposes described herein, two pathsare considered to be coextensive up to the most recent locations alongthe paths where they can be connected by a line segment d_(i) extendingperpendicular to each path.

In addition, for more accurate results, it is also desirable that thecoextensive portions of the vehicle path histories being evaluated forparallelism should be of at least a predetermined minimum length. In oneembodiment, the portion L_(eval) of each total vehicle path length thatis evaluated for parallelism satisfies the following relationship:

L_(eval)>L_(min)   (3)

where L_(min) is a parameter which may be determined according to therequirements of a given application. In a particular embodiment,L_(min)=5 L_(max). Thus, in the example shown in FIG. 4, if parallelismof path A to path B is evaluated over the portion of path A includingline segments l₁-l₄, then the above condition is met where:

L _(eval)=(|l ₁ |+|l ₂ |+|l ₃ |+|l ₄|)>L _(min)   (4)

The path histories of any surrounding vehicles SV are determined in amanner similar to that used for the path history of ego-vehicle 12. FIG.4 illustrates one method of calculating a path history B of asurrounding vehicle SV1 with respect to the ego vehicle 12. On acontinuous basis, ego-vehicle sensors 14 detect the position ofsurrounding vehicle SV1 with respect to ego-vehicle 12. FIG. 4 shows thesuccessive positions P_(i)′ of vehicle SV1 in relation to ego-vehicle 12based on sensor readings taken at successive points in time t₁′, t₂′,t₃′, etc. For example, the position of SV1 at time t₁′ is shown as P₁′,which represents the x, y, and z coordinates of vehicle SV1 with respectto ego-vehicle 12 based on sensor readings taken at a time t₁′.

The SV1 positional data from sensors 14 is passed to computing device18, which stores the positional data. The data is also processed in amanner similar to that previously described for ego-vehicle 12, togenerate a digital representation of the path history B of vehicle SV1in relation to the path history of the ego vehicle. Computing device 18may also process the SV1 positional data to calculate associatedvelocities and accelerations of SV1 with respect to the ego vehicle.This procedure may be executed for any surrounding vehicle.

In a particular embodiment, data acquisition for both the ego-vehicleand one or more of the surrounding vehicle is coordinated andtime-correlated, so that the sensor readings needed for pathdeterminations of the ego-vehicle and the one or more surroundingvehicle(s) are taken at the same points in time.

A vehicle path may be calculated using any suitable non-varying featureof the vehicle as a reference. For example, in an ego-vehicle using aradar detection system and following behind a surrounding vehicle, thereference on the surrounding vehicle may be taken as a centerline of anenvelope defined by opposite lateral sides of the vehicle.Alternatively, the reference may be taken as a side (or a feature of aside) of the vehicle. Location points P used for generating the vehiclepath approximations are then assumed to lie on the vehicle measurementreference location.

After the path histories of the ego vehicle and vehicle SV1 have beendetermined, the path histories may be compared and evaluated forparallelism using the criteria set forth herein. As the vehicle pathhistories calculated by the computing device are continuous andup-to-date representations of the various vehicle paths, the results ofthe path parallelism assessments are used to detect lane changes by thevarious surrounding vehicles. That is, when the paths of any surroundingvehicles traveling adjacent the ego-vehicle or side-by-side with eachother are no longer deemed to be parallel according to the criteria setforth herein, computing device 18 sends a warning to the ego-vehiclealerting its occupants that one of the surrounding vehicles is executinga lane change.

FIG. 4 shows one example of a method for evaluating parallelism of anytwo given path histories A and B. Distances d_(i) represent minimumdistances between path A and path B, measured from correspondinglocations P_(i) along line segments representing path A where positionalsensor readings were taken by the INS. For example, d₁ is a minimumdistance between path A and path B, measured from a first location P₁along a line segment l₁ of path A where positional sensor readings weretaken by the INS. Referring to FIG. 4, the computing device 18calculates a minimum distance d₁. The computing device 18 alsocalculates a minimum distance d₄ between path A and path B, measuredfrom a second location P₄ along path A where positional sensor readingswere taken by the INS. The minimum distances between the vehicle pathsmay be measured along coextensive portions of the paths for which themost recent or latest data is available, in order to detect a lanedeparture as soon as possible. Also, it will be noted that the spacingbetween the paths A and B may be calculated by measuring minimumdistances from path A to path B or from path B to path A.

As stated previously, the minimum lengths L_(eval) of the portions ofthe paths A and B over which parallelism is measured should meet thecriterion L_(eval)>L_(min) as previously described. Thus, in a casewhere parallelism is measured as described above, the portionL_(eval)=l₁+l₂+l₃+l₄ of path A along which parallelism is measured wouldneed to meet the criterion L_(eval)=(|l₁|+|l₂|+|l₃|+|l₄|)>L_(min). Thecomputing device then calculates the value of |(d₄−d₁). The computingdevice then evaluates the value |(d₄−d₁)|in accordance with therelationship:

Max |(d _(k) −d _(i))|<A _(max) for all i,k in {1, 2, 3, 4, etc.}  (5)

where A_(max) is a parameter which may be determined according to therequirements of a given application. In a particular embodiment,A_(max)=15 centimeters. In another particular embodiment, A_(max)=30centimeters. In yet another particular embodiment, A_(max)=50centimeters.

If the absolute value of the difference in the minimum spacing betweenthe vehicle paths A and B at locations P₁ to P₄ varies by less thanA_(max) (i.e., if the distance between the paths A and B remainsrelatively constant throughout the most recent portions of the vehicles'paths), then it is deemed unlikely that both vehicles are changing lanessimultaneously. It is also deemed unlikely that any one of the twovehicles was changing lanes. Therefore, the computing device 18determines that no lane change is occurring. However, if the absolutevalue of the difference in the spacing between the vehicle paths A and Bat locations P₁ to P₄ varies by A_(max) or more, the computing devicedetermines that the paths A and B followed by the associated vehiclesare no longer parallel. In this case, it the computing device 18 assumesthat one of the vehicles is changing lanes. Thus, the parameter A_(max)defines a zone of permissible variation of the spacing between paths Aand B. If it is determined that one of the vehicles is changing lanes, asuitable warning or alert can be transmitted to the ego-vehicleoccupants via HMI 109.

FIG. 5 shows additional aspects of the system embodiments describedherein. Because the lane departure detection system 200 is evaluatingparallelism of estimated paths of the ego-vehicle and/or surroundingvehicles, a lane departure may be detected for any surrounding vehicle,traveling either in front of or behind the ego-vehicle, assuming thatthe positional and movement information necessary for calculating thevehicle path is available to the ego-vehicle computing device 18.

In FIG. 5 for example, the lane departure detection system 200 maydetect a lane departure of a surrounding vehicle SV4 traveling behindego-vehicle 12 in an adjacent lane, based on a determination of thespacing between the estimated vehicle paths A′ and B′, as previouslydescribed. Also, the lane departure detection system 200 may detect alane departure of a surrounding vehicle SV5 traveling ahead ofego-vehicle 12 in an adjacent lane, based on a determination of thespacing between the estimated vehicle paths A′ and B′, as previouslydescribed.

FIG. 6 is a flow diagram illustrating a method for detecting a lanedeparture of a vehicle traveling in a lane adjacent an ego-vehicle, in ascenario such as shown in FIG. 4 or FIG. 5.

In block 510, computing device 18 acquires ego vehicle operationalinformation needed to calculate the ego vehicle path. In block 520,computing device 18 acquires surrounding vehicle operational informationneeded to calculate the surrounding vehicle path. This may be done forone or more surrounding vehicles simultaneously. Also, the informationacquisition in blocks 510 and 520 may be executed simultaneously. Inblock 530, using the ego-vehicle operational information, computingdevice 18 calculates the ego-vehicle path. In block 540, using thesurrounding vehicle operational information, computing device 18calculates the path(s) of the surrounding vehicle(s). The calculationsin blocks 530 and 540 may be done simultaneously.

In step 550, computing device 18 compares the various vehicle paths andevaluates the paths for parallelism. In block 560, if it is determinedthat the vehicle paths being evaluated do not satisfy the parallelismcriterion set for the relation (5), a warning or alert is transmitted tothe ego-vehicle occupant(s). Then acquisition of operational informationfrom the ego-vehicle and surrounding vehicles continues. If it isdetermined that the vehicle paths being evaluated satisfy theparallelism criterion set for the relation (5), acquisition ofoperational information from the ego-vehicle and surrounding vehiclescontinues.

FIG. 7 shows another aspect of the detection system embodimentsdescribed herein. FIG. 7 shows a schematic view similar to the viewshown in FIG. 4, illustrating two vehicle paths AA and BB. In FIG. 7,the ego-vehicle lane departure detection system 200 is used to detect alane change of one of two surrounding vehicles SV1 and SV2 travelingside-by-side or adjacent each other, with the ego vehicle 12 travelingalong the same lane or portion of road as SV2 and behind SV2. Paths AAand BB represent the paths of the two surrounding vehicles SV1 and SV2.In this embodiment, the ego-vehicle computing device 18 receives sensorinformation from (or relating to) both of surrounding vehicles SV1 andSV2 and calculates the paths of these surrounding vehicles in relationto the ego-vehicle, in the manner previously described.

As the ego-vehicle 12 and the surrounding vehicles SV1 and SV2 travelalong the road in direction G, ego-vehicle sensors continuously detectthe positions of the surrounding vehicle at various points along theroad in accordance with the ego-vehicle sensor sampling rates. FIG. 7shows successive positions PP_(i)′ of vehicle SV1 in relation toego-vehicle 12 based on sensor readings taken at successive points intime t₁′, t₂′, t₃′, etc. For example, the position of the vehicle SV1 attime t₁′ is shown as PP₁′, which represents the x, y, and z coordinatesof vehicle SV1 with respect to ego-vehicle 12 based on ego-vehiclesensor readings taken at a time t₁′. Similarly, FIG. 7 also showssuccessive positions PP_(i) of vehicle SV2 in relation to ego-vehicle 12based on sensor readings taken at successive points in time t₁, t₂, t₃,etc. For example, the position of the vehicle SV2 at time t₁ is shown asPP₁, which represents the x, y, and z coordinates of vehicle SV2 withrespect to ego-vehicle 12 based on ego-vehicle sensor readings taken ata time t₁. As described with regard to FIG. 4, during movement of thevehicle SV2, the computing device 18 may process the vehicle positionaldata to connect time-successive calculated vehicle positions PP_(i) withline segments ll_(i) as shown in FIG. 7. The aggregate of the connectedline segments ll_(i) (i.e., segments ll₁, ll₂, ll₃ and ll₄ as shown) isused, in one embodiment, to define the vehicle path geometry (i.e., pathAA) in terms of an up-to-date representation of a positional or pathhistory of the vehicle. Also, during movement of the vehicle SV1, thecomputing device 18 may process the vehicle positional data to connecttime-successive calculated vehicle positions PP_(i)′ with line segmentsll_(i)′ as shown in FIG. 7. The aggregate of the connected line segmentsll_(i)′ (i.e., segments ll₁′, ll₂′, ll₃′ and ll₄′ as shown) is used, inone embodiment, to define the vehicle path geometry (i.e., path BB) interms of an up-to-date representation of a positional or path history ofthe vehicle.

Also, similar to the embodiment shown in FIG. 4, distances dd_(i)represent minimum distances between path AA and path BB, measured fromcorresponding locations PP_(i) along line segments representing path AAwhere sensor readings on the position of the vehicle SV2 were taken. Forexample, dd₁ is a minimum distance between path AA and path BB, measuredfrom a first location PP₁ along a line segment ll₁ of path AA wheresensor readings on the position of the vehicle SV2 were taken.

On a continuous basis, the successive positions of each of surroundingvehicles SV1 and SV2 with respect to the ego-vehicle are processed togenerate representations of the paths AA and BB of the surroundingvehicles. Successive sensor measurement locations relating to each ofvehicles SV1 and SV2 are connected with straight line segments aspreviously described to generate vehicle path approximations.

Parallelism of paths AA and BB are evaluated according to the criteriaand in a manner similar to that described previously. It will be notedthat the spacing between the paths AA and BB may be calculated bymeasuring minimum distances from path AA to path BB or from path BB topath AA. The minimum distances between the paths AA and BB continue tobe estimated as previously described, as the vehicle SV1 and SV2 travelalong the road ahead of ego-vehicle 12. The minimum distances betweenthe paths are measured along the coextensive portions of the paths forwhich the most recent or latest data is available, in order to detect alane departure as soon as possible.

If the absolute value of the of the difference in the minimum spacingbetween the vehicle paths A and B along the most recent portions of thevehicle paths varies by more than A_(max) as set forth in relation (5)above, the computing device determines that the paths AA and BB followedby the associated vehicles are no longer parallel. In this case, it thecomputing device 18 assumes that one of the vehicles SV1 and SV2 ischanging lanes. The vehicles then may be labeled as (or given the statusof) “lane changing”. However, if the absolute value of the variation ofthe distance between the vehicle paths A and B as calculated above alongthe most recent portions of the vehicle paths does not equal or exceedA_(max) as set forth in relation (5) above, then it is deemed unlikelythat both vehicles are changing lanes simultaneously and also unlikelythat any one of the two vehicles is changing lanes. In this case, thecomputing device determines that the paths AA and BB followed by theassociated vehicles are still parallel, and the vehicles may be labeledas (or given the status of) “non-lane changing”.

Thus, the lane departure detection method illustrated in FIG. 7 includessteps of locating a pair of vehicles SV1 and SV2 traveling in adjacentlanes and side-by-side, and monitoring, along a portion of the vehicles'paths AA and BB including the most recent or latest known positions ofthe vehicles, the distance between the vehicles. If the vehicleseparation distance remains relatively constant over the latest portionof the vehicles' paths, then it is deemed unlikely that both vehiclesare changing lanes simultaneously and also unlikely that any one of thetwo vehicles is changing lanes. In this case, the vehicles may belabeled as (or given the status of) “non-lane changing”. Alternatively,if the vehicle separation distance varies by more than a predeterminedamount over the latest portion of the vehicles' paths, then it isdetermined that one of the vehicles is changing lanes. In this case, thevehicles may be labeled as (or given the status of) “lane changing”.

FIG. 8 is a flow diagram illustrating a method for detecting a lanedeparture of one of two surrounding vehicles traveling ahead of theego-vehicle, in a scenario such as shown in FIG. 7. In block 610,computing device 18 acquires surrounding operational information forvehicle SV1 needed to calculate the surrounding vehicle path. In block620, computing device 18 acquires surrounding operational informationfor vehicle SV2 needed to calculate the surrounding vehicle path. Also,the information acquisition in blocks 610 and 620 may be executedsimultaneously.

In block 630, using the ego-vehicle operational information, computingdevice 18 calculates the ego-vehicle path. In block 640, using thesurrounding vehicle operational information, computing device 18calculates the path(s) of the surrounding vehicle(s). The calculationsin blocks 630 and 640 may be done simultaneously. In step 650, computingdevice 18 compares the various vehicle paths and evaluates the paths forparallelism. In block 660, if it is determined that the vehicle pathsbeing evaluated do not satisfy the parallelism criterion set for therelation (5), a warning or alert is transmitted to the ego-vehicleoccupant(s). Then acquisition of operational information from thesurrounding vehicles continues. If it is determined that the vehiclepaths being evaluated satisfy the parallelism criterion set for therelation (5), acquisition of operational information from thesurrounding vehicles continues.

In another example, the system can provide information to the driverwhich might indicate that the surrounding lane-changing vehicle istrying to avoid an obstacle or a slow moving vehicle and prepare thedriver to encounter such a situation. In addition, in cases where theego-vehicle receives position and movement information from a vehicledriving ahead of the ego-vehicle in the same lane, the receivedinformation provides the ego-vehicle driver with a view of the lane pathand road conditions ahead of the ego-vehicle.

In the manner described herein, the embodiments of the lane departuredetection system enable the detection of lane changes of surroundingvehicles without the use of lane markers. In addition, the systemenables detection of a vehicle lane by continuously approximating thepath of the vehicle and also the path of one other vehicle which is usedfor comparison purposes. The most recent coextensive portions of thevehicle paths are continuously evaluated for parallelism, as describedabove.

In addition, it may be seen that the path geometries of vehicles whichare not changing lanes may serve as confirmations or independentindications of the paths of actual or current lanes in which vehiclesare traveling. This is helpful in cases where the actual lanes aredifferent from marked lanes (i.e., in cases where the actual travellanes differ from marked lanes due, for example, to construction,temporary lane closure, or accident investigation). The path geometriesof vehicles which are not changing lanes may also indicate the roadgeometry where road-edges or lane markers cannot be seen or sensedvisually or using a camera system, and in cases where the projectedvehicle paths are received on a GPS receiver in the form of routeinformation.

In another aspect, in a vehicle configured for a degree of autonomousoperation, the computing device may be configured to operate theego-vehicle in response to the detection of a surrounding vehicle lanechange. For example, referring to FIG. 5, if ego-vehicle 12 detects thatvehicle SV5 is changing lanes, additional sensors may determine thatvehicle SV5 is moving toward ego-vehicle path A′. The ego-vehiclecomputing device 18 may be configured to execute any of a variety ofcommands designed to increase occupant safety in such cases. Forexample, the ego-vehicle 18 may slow to increase the spacing between theego-vehicle and SV5. Thus, detection of surrounding vehicle lane changesusing the system described herein can aid in planning the future path ofan ego-vehicle and also help estimate geometry of the lane on the road.

In another aspect of the embodiments described herein, the surroundingvehicle operational information needed for calculation of the vehiclepaths may be obtained from sources other than the ego vehicle sensors.For example, the information may be transmitted to the ego-vehicle fromroadside devices, a GPS system, or from the surrounding vehicle itselfusing a V2V or short-range vehicle communication system.

FIG. 9 shows various possible alternative implementations of the lanedeparture detection system.

Referring to FIG. 9, in another embodiment, a computing device 118having the configuration and capabilities described herein is located ina roadside device 19. Position and movement information relating to allof the vehicles traveling on the road is sent to the roadside device 19for processing by the computing device 118. The vehicle information maybe acquired by sensors located in the roadside device 19. Alternatively,the vehicle information may be transmitted to the roadside device 19from one or more of the vehicles 12 and SV. For example, the informationrelating to each vehicle may be gathered and/or calculated by the INS inthe particular vehicle. Alternatively, the roadside device 19 mayreceive the vehicle information may be received from GPS system 52. In aparticular embodiment, roadside device 19 is in communication with aremote terminal 50 configured to receive information (including lanedepartures) relating to the ego-vehicle 12 and any surrounding vehiclesSV.

In one embodiment, position and movement information relating to one ormore surrounding vehicles SV is acquired by sensors in a roadside device19 and transmitted to the ego-vehicle 12 for processing in the mannerdescribed herein.

In another embodiment, position and movement information relating to oneor more surrounding vehicles SV is acquired by (or received from the SVby) a GPS system 52. This information may then be transmitted from theGPS system to the ego-vehicle 12 for processing in the manner describedherein.

While the aspects described herein have been discussed with respect tospecific examples including various modes of carrying out aspects of thedisclosure, those skilled in the art will appreciate that there arenumerous variations and permutations of the above described systems andtechniques that fall within the spirit and scope of the invention.

What is claimed is:
 1. A computer-implemented method for operating avehicle configured for autonomous operation, comprising steps of: (1) ina lane departure detection computing device of a roadside device: (a)calculating a first path geometry relating to a first vehicle travelingin a first lane; (b) calculating a second path geometry relating to asecond vehicle traveling in a second lane different from the first lane;(c) evaluating coextensive portions of the first path geometry and thesecond path geometry for parallelism; (d) if the coextensive portions ofthe first path geometry and the second path geometry evaluated forparallelism are parallel, repeating steps (a)-(d); (e) if thecoextensive portions of the first path geometry and the second pathgeometry evaluated for parallelism are not parallel, determining thatone of the first vehicle and the second vehicle is executing a lanedeparture; and (2) operating the vehicle configured for autonomousoperation responsive to the determination that one of the first vehicleand the second vehicle is executing a lane departure.
 2. The method ofclaim 1, wherein the step of evaluating coextensive portions of thefirst path geometry and the second path geometry for parallelismincludes the steps of: calculating a first minimum spacing of the secondpath geometry from the first path geometry at a first location along thefirst path geometry; calculating a second minimum spacing of the secondpath geometry from the first path geometry at a second location alongthe first path geometry spaced apart from the first location;determining a difference between the first minimum spacing and thesecond minimum spacing; and if the difference is equal to or greaterthan a predetermined value, determining that the coextensive portions ofthe first path geometry and the second path geometry residing betweenthe first minimum spacing and the second minimum spacing are notparallel.
 3. The method of claim 1 wherein the step of calculating thefirst path geometry comprises the step of measuring position coordinatesof the first vehicle at a rate such that a maximum distance betweensensor measurement locations is a predetermined value.
 4. The method ofclaim 1 wherein the step of evaluating coextensive portions of the firstpath geometry and the second path geometry for parallelism comprises thestep of evaluating coextensive portions of the first path geometry andthe second path geometry when the first path geometry and the secondpath geometry are spaced apart at least a minimum predetermineddistance.
 5. The method of claim 1 wherein the step of evaluatingcoextensive portions of the first path geometry and the second pathgeometry for parallelism comprises the step of evaluating coextensiveportions of the first path geometry and the second path geometry forparallelism over at least a predetermined length of the first pathgeometry and the second path geometry.
 6. The method of claim 1 furthercomprising the step of, prior to the step of calculating the first pathgeometry relating to the first vehicle, receiving information usable forcalculating the first path geometry from the first vehicle.
 7. Themethod of claim 6 further comprising step of, prior to the step ofcalculating the second path geometry relating to the second vehicle,receiving information usable for calculating the second path geometryfrom the second vehicle.
 8. The method of claim 6 wherein the roadsidedevice includes one or more sensors configured for acquiring informationusable for calculating the second path geometry, and wherein the methodfurther comprises the step of, prior to the step of calculating thesecond path geometry relating to the second vehicle, acquiring vehicleinformation usable for calculating the second path geometry by the oneor more sensors of the roadside device usable for calculating the secondpath geometry.
 9. The method of claim 1 wherein the roadside deviceincludes one or more sensors configured for acquiring information usablefor calculating the first path geometry, and wherein the method furthercomprises the step of, prior to the step of calculating the first pathgeometry relating to the first vehicle, acquiring vehicle informationusable for calculating the first path geometry by the one or moresensors of the roadside device configured for acquiring informationusable for calculating the first path geometry.
 10. The method of claim9 further comprising the step of, prior to the step of calculating thesecond path geometry relating to the second vehicle, receivinginformation usable for calculating the second path geometry from thesecond vehicle.
 11. The method of claim 9 wherein the roadside deviceincludes one or more sensors configured for acquiring information usablefor calculating the second path geometry, and wherein the method furthercomprises the step of, prior to the step of calculating the second pathgeometry relating to the second vehicle, acquiring vehicle informationusable for calculating the second path geometry by the one or moresensors of the roadside device configured for acquiring informationusable for calculating the second path geometry.
 12. The method of claim1 further comprising the step of, prior to at least one of the steps ofcalculating the first path geometry relating to the first vehicletraveling in the first lane and calculating the second path geometryrelating to the second vehicle traveling in the second lane, receiving,from a GPS system, information usable for calculating the at least oneof the first path geometry relating to the first vehicle traveling inthe first lane and the second path geometry relating to the secondvehicle traveling in the second lane.
 13. The method of claim 1 whereinthe vehicle configured for autonomous operation is a third vehicleseparate from the first vehicle and the second vehicle.
 14. The methodof claim 1 wherein the vehicle configured for autonomous operation isone of the first vehicle and the second vehicle.
 15. The method of claim1 wherein the vehicle configured for autonomous operation includes avehicle computing device configured for operating the vehicle configuredfor autonomous operation responsive to a determination by the lanedeparture detection computing device that one of the first vehicle andthe second vehicle is executing a lane departure, wherein the methodfurther comprises transmitting, to the vehicle configured for autonomousoperation, information including the determination that one of the firstvehicle and the second vehicle is executing a lane departure, andwherein the step of operating the vehicle configured for autonomousoperation comprises operating the vehicle configured for autonomousoperation by the vehicle computing device.
 16. The method of claim 1wherein the step of operating the vehicle configured for autonomousoperation comprises operating the vehicle by the lane departuredetection computing device of the roadside device.
 17. The method ofclaim 1 further comprising the step of, after step (1)(c) and beforestep (1)(d): if the coextensive portions of the first path geometry andthe second path geometry evaluated for parallelism are parallel,determining at least a portion of a road geometry from the coextensiveportions of the first path geometry and the second path geometryevaluated for parallelism.
 18. A non-transitory computer readable mediumwith computer executable instructions stored thereon executed by aprocessor to perform a method of operating a vehicle configured forautonomous operation, the method comprising: (a) calculating a firstpath geometry relating to a first vehicle traveling in a first lane; (b)calculating a second path geometry relating to a second vehicletraveling in a second lane different from the first lane; (c) evaluatingcoextensive portions of the first path geometry and the second pathgeometry for parallelism; (d) if the coextensive portions of the firstpath geometry and the second path geometry evaluated for parallelism areparallel, repeating steps (a)-(d); (e) if the coextensive portions ofthe first path geometry and the second path geometry evaluated forparallelism are not parallel, determining that one of the first vehicleand the second vehicle is executing a lane departure; and (f) operatingthe vehicle configured for autonomous operation responsive to thedetermination that one of the first vehicle and the second vehicle isexecuting a lane departure.
 19. A lane departure detection computingdevice for a roadside device, the computing device comprising one ormore processors for controlling operation of the computing device, and amemory for storing data and program instructions usable by the one ormore processors, wherein the one or more processors are configured toexecute instructions stored in the memory to: calculate a first pathgeometry relating to a first vehicle traveling in a first lane;calculate a second path geometry relating to a second vehicle travelingin a second lane different from the first lane; evaluate coextensiveportions of the first path geometry and the second path geometry forparallelism; if the coextensive portions of the first path geometry andthe second path geometry evaluated for parallelism are not parallel,determine that one of the first vehicle and the second vehicle isexecuting a lane departure; and operate a vehicle configured forautonomous operation responsive to the determination that one of thefirst vehicle and the second vehicle is executing a lane departure.